Systems and methods of choroidal neovascularization detection using optical coherence tomography angiography

ABSTRACT

Disclosed are systems and methods to automatically detect choroidal neovascularization (CNV) in the outer retina using OCT angiography. Further disclosed are methods of removing projection artifacts from the outer retina and for combining brightness, orientation, and position information in a context-aware saliency model to quantify CNV area in OCT angiograms.

ACKNOWLEDGEMENT OF GOVERNMENT SUPPORT

This invention was made with the support of the United States governmentunder the terms of Grant Number R01 EY024544 awarded by the NationalInstitutes of Health. The United States government has certain rights inthis invention.

FIELD

Generally, the field involves methods of using optical coherencetomography (OCT) in angiography. More specifically, the field involvesmethods of processing OCT angiography images to detect and characterizechoroidal neovascularization.

BACKGROUND

Age-related macular degeneration (AMD) is the leading cause of blindnessin people 50 years or older in the developed world (Pascolini D et al,Ophthalmic Epidemiol 11, 67-115 (2004) and Jaeger R et al, N Engl J Med358, 2602-2617 (2008); both of which are incorporated by referenceherein). The advanced, neovascular form of AMD is characterized by thepresence of choroidal neovascularization (CNV), pathologic new vesselsfrom the choroid that grow into the avascular outer retina throughbreaks in Bruch's membrane (BM). CNV can lead to subretinal hemorrhage,fluid exudation, lipid deposition, detachment of the retinal pigmentepithelium from the choroid, fibrotic scars, or a combination of these(Jaeger R et al, 2008 supra; De Jong P, N Engl J Med 355, 1474-1485(2006); Donoso L et al, Surv Ophthalmol 51, 137-152 (2006); Stanga P etal, Ophthalmol 110, 15-21 (2003); incorporated by reference herein).Fluorescein (FA) and/or indocyanine green angiography (ICGA) havetraditionally been used to detect and assess CNV in the clinic. However,these techniques are two-dimensional (2D) and involve intravenous dyeinjections, which can lead to nausea and anaphylaxis (Lopez-Saez M etal, Ann Allergy Asthma Immunol 81, 428-430 (1998); incorporated byreference herein).

Optical coherence tomography (OCT) is a noninvasive, depth resolved,volumetric imaging technique that is commonly used to visualize retinalmorphology (Huang D et al, Science 254, 1178-1181 (1991); incorporatedby reference herein). A limitation of conventional structural OCT isthat it cannot be used to detect blood flow or discriminate vasculartissue from surroundings. To address this limitation, several OCTangiography methods have been proposed to identify blood flow at themicrocirculation level (An L et al, Opt Express 16, 11438-11452 (2008);Yasuno Y et al, “Opt Express 15, 6121-6139 (2007); Grulkowsk I et al,Opt Express 17, 23736-23754 (2009); Fingler J et al, Opt Express 17,22190-22200 (2009); Liu G et al, Opt Express 19, 3657-3666 (2011);incorporated by reference herein). Among these OCT angiography methods,the split-spectrum amplitude-decorrelation angiography (SSADA) algorithmis able to distinguish blood flow from static tissues based on detectingthe reflectance amplitude decorrelation over consecutive cross-sectionalB-scans at the same location (Jia Y et al, Opt Express 20, 4710-4725(2012); Gao S et al, Opt Lett 40, 2305-2308 (2015); incorporated byreference herein). Moreover, segmentation of SSADA-based OCT angiogramscan identify CNV as blood flow in the outer retina, a region devoid ofblood flow in healthy eyes (Jia Y et al, Ophthalmology 121, 1435-1444(2014); Jia Y et al Proc Natl Acad Sci USA 112, E2395-2402 (2015); deCarlo T et al, Ophthalmology 122, 1228-1238 (2015); Spaide R, Am JOphthalmol 160, 6-16 (2015); Kuehlewein L et al, Eye (Lond) 29, 932-935(2015); incorporated by reference herein). Despite these advances in OCTangiography delineation of CNV lesions from such datasets remains achallenge. The simplest method involves manual delineation by anexperienced expert, but this approach is subjective, operator intensive,and time-consuming. Thus, a reliable and robust automatic detectionmethod for quantifying the CNV lesion is needed in order to maximize theclinical utility of OCT angiography in the diagnosis of CNV andevaluation of the therapeutic effect of different treatments.

SUMMARY

Disclosed herein is an image processing method for use in OCTangiography that detects and quantifies CNV in the outer retina of theeye. The method involves receiving a set of cross sectional angiograms,separating that set of angiograms into two distinct subsets representingthe inner retina and outer retina, and then projecting the maximum flowvalues in each subset to produce a pair of 2D en face angiograms,referred to hereafter as the inner retina angiogram and the outer retinaangiogram. The inner retina angiogram is filtered to remove noise andthen subtracted from the outer retina angiogram to reduce projectionartifacts from the deeper outer retina angiogram. The resultant outerretina angiogram image is submitted to a pattern recognition analysiswherein a multiscale saliency map is generated. This saliency map isprocessed using a nonlinear filtering approach to smooth within thetarget region while enhancing edges within the saliency map. Finally abinary vascular mask is generated from the edge-enhanced saliency mapusing thresholding and morphological operations. This binary vascularmask is applied to the original 2D en face outer retina angiogram toextract the CNV area contained therein.

Also disclosed herein are tests of the algorithm performance in terms ofrepeatability and in comparison of automated versus manual delineation.Further, using the manual delineations as the standard, false positiveerror and false negative error rates are calculated for the disclosedalgorithm.

It is an object of the invention to overcome problems with projectionartifacts that cast shadows from the more superficial inner retina ontothe deeper outer retina where CNVs may manifest.

It is an object of the invention to overcome problems withdistinguishing the boundary between CNV and non-CNV background byincorporating a multiscale saliency model to enhance contrast.

It is an object of the invention to output a visualization and aquantification of CNV area that a clinician could use for diseaseassessment and monitoring.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the disclosed subject matter, nor is it intendedto be used to limit the scope of the disclosed subject matter.Furthermore, the disclosed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a flowchart of a method of detecting choroidalneovascularization (CNV).

FIG. 2 is a pictorial flowchart depicting the segmentation algorithmdescribed in (Jia Y et al, 2014 supra) and the disclosed method forautomated saliency-based segmentation of choroidal neovascularization(CNV). A) Original outer retina angiogram of a patient with neovascularage-related macular degeneration (AMD) showing artifacts and CNV. (B1)Inner retinal large vessel mask used to subtract large vesselprojections from the outer retina. (B2) Filtered inner retina used tosubtract artifacts from the outer retina. (C1) Outer retina with largevessel projections removed. (C2) Outer retina with artifacts removed.(D1) Gaussian filtering to reduce the remaining artifacts. (D2) Saliencymap showing the CNV region. (E1) CNV area obtained by a thresholdoperator. (E2) CNV area obtained by multiplying the CNV membrane maskand original en face outer retinal angiogram and using a thresholdoperator. (F1) Multi-scale saliency results showing intermediate resultsof calculating the saliency map. (F2) Post-processing procedureincluding nonlinear filtering to enhance the boundaries and smooth thesaliency map, Otsu's method for determining the threshold, andmorphological operations for obtaining the CNV membrane mask. (A, B2,C1, C2, D1) The display scale of decorrelation values ranges from 0.025to 0.25.

FIG. 3 is a grid of images depicting en face OCT angiograms retinalangiograms from all participants except for participant #5 which isshown in FIG. 4. The top row (A1-6) shows the en face maximum flowprojection angiogram from the outer retinal slab without any additionalprocessing. The second row (B1-6) shows the results of manualdelineation of CNV by an expert human. The third row (C1-6) shows theresults from the automated saliency algorithm. The bottom row (D1-6)shows the results from the previous automated algorithm. CNV areas, asdelineated by a grader or algorithm, are shown below each processedimage. The display scale of decorrelation values ranges from 0.025 to0.25 for all images.

FIG. 4 is a grid of images depicting en face OCT angiograms from thecase (participant #5) where there was the greatest difference in CNVarea between the saliency algorithm and expert manual grading. (A) Innerretinal angiogram. (B) Outer retinal angiogram without any additionalprocessing. (C) Manual delineation of CNV by an expert human. (D)Automated saliency algorithm. Yellow arrows highlight points of interestfor comparison between C and D. (E) Previous automated algorithm. CNVareas, as delineated by a grader or algorithms, are shown below eachprocessed image. (F) Choriocapillaris angiogram without any additionalprocessing. The display scale of decorrelation values ranges from 0.025to 0.25 for all images.

FIG. 5 is a set of images depicting the results of image processingoperations used to delineate CNV in OCT volumetric images. (A1) Originalouter retina angiogram. (A2) Outer retina angiogram with inner retinallarge vessel subtracted leaving motion artifacts and small vesselprojections. (A3) Outer retina angiogram with both large and smallretinal vessels subtracted. (B1) Saliency map computed from A1 shows theCNV outline but is contaminated with large retinal vessel pattern. (B2)Saliency map computed from A2 shows CNV outline but is cluttered with abackground haze. (B3) Saliency map computed from A3 shows a clean CNVpattern. (A1 to A3) The display scale of decorrelation values rangesfrom 0.025 to 0.25.

FIG. 6 is a schematic of a system for processing OCT angiography data inaccordance with the disclosure.

FIG. 7 is an example of a computing system in accordance with thedisclosure.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which are shownby way of illustration embodiments that can be practiced. It is to beunderstood that other embodiments can be utilized and structural orlogical changes can be made without departing from the scope. Therefore,the following detailed description is not to be taken in a limitingsense, and the scope of embodiments is defined by the appended claimsand their equivalents.

Various operations can be described as multiple discrete operations inturn, in a manner that can be helpful in understanding embodiments;however, the order of description should not be construed to imply thatthese operations are order dependent.

The description may use the terms “embodiment” or “embodiments,” whichmay each refer to one or more of the same or different embodiments.Furthermore, the terms “comprising,” “including,” “having,” and thelike, as used with respect to embodiments, are synonymous.

In various embodiments, structure and/or flow information of a samplecan be obtained using OCT (structure) and OCT angiography (flow)imaging-based on the detection of spectral interference. Such imagingcan be two-dimensional (2-D) or three-dimensional (3-D), depending onthe application. Structural imaging can be of an extended depth rangerelative to prior art methods, and flow imaging can be performed in realtime. One or both of structural imaging and flow imaging as disclosedherein can be enlisted for producing 2-D or 3-D images.

Unless otherwise noted or explained, all technical and scientific termsused herein are used according to conventional usage and have the samemeaning as commonly understood by one of ordinary skill in the art whichthe disclosure belongs. Although methods, systems, andapparatuses/materials similar or equivalent to those described hereincan be used in the practice or testing of the present disclosure,suitable methods, systems, and apparatuses/materials are describedbelow.

All publications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including explanation ofterms, will control. In addition, the methods, systems, apparatuses,materials, and examples are illustrative only and not intended to belimiting.

In order to facilitate review of the various embodiments of thedisclosure, the following explanation of specific terms is provided:

A-scan: A reflectivity profile that contains information about spatialdimensions and location of structures within an item of interest. AnA-scan is an axial scan directed along the optical axis of the OCTdevice and penetrates the sample being imaged. The A-scan encodesreflectivity information (for example, signal intensity) as a functionof depth.

B-scan: A cross-sectional tomograph that can be achieved by laterallycombining a series of axial depth scans (i.e., A-scans) in thex-direction or y-direction. A B-scan encodes planar cross-sectionalinformation from the sample and is typically presented as an image.Thus, a B-scan can be called a cross sectional image.

Dataset: As used herein, a dataset is an ordered-array representation ofstored data values that encodes relative spatial location inrow-column-depth (x-y-z axes) format. In the context of OCT, as usedherein, a dataset can be conceptualized as a three dimensional array ofvoxels, each voxel having an associated value (for example, an intensityvalue or a decorrelation value). An A-scan corresponds to a set ofcollinear voxels along the depth (z-axis) direction of the dataset; aB-scan is made up of set of adjacent A-scans combined in the row orcolumn (x- or y-axis) directions. Such a B-scan can also be referred toas an image, and its constituent voxels referred to as pixels. Acollection of adjacent B-scans can be combined form a 3D volumetric setof voxel data referred to as a 3D image. In the system and methodsdescribed herein, the dataset obtained by an OCT scanning device istermed a “structural OCT” dataset whose values can, for example, becomplex numbers carrying intensity and phase information. Thisstructural OCT dataset can be used to calculate a corresponding datasettermed an “OCT angiography” dataset of decorrelation values reflectingflow within the imaged sample. There is a one-to-one correspondencebetween the voxels of the structural OCT dataset and the OCT angiographydataset. Thus, values from the datasets can be “overlaid” to presentcomposite images of structure and flow (e.g., tissue microstructure andblood flow) or otherwise combined or compared.

En Face angiogram: OCT angiography data can be presented as a projectionof the three dimensional dataset onto a single planar image called an enface angiogram (Wallis J et al, Med Imaging IEEE Trans 8, 297-230(1989); Wang R K et al, 2007 supra; Jia Y et al, 2012 supra);incorporated by reference herein). Construction of such an en faceangiogram requires the specification of the upper and lower depthextents that enclose the region of interest within the retina OCT scanto be projected onto the angiogram image. These upper and lower depthextents can be specified as the boundaries between different layers ofthe retina (e.g., the voxels between the inner limiting membrane andouter plexiform layer can be used to generate a 2D en face angiogram ofthe inner retina). Once generated, the en face angiogram image may beused to quantify various features of the retinal vasculature asdescribed herein. This quantification typically involves the setting ofa threshold value to differentiate, for example, the pixels thatrepresent active vasculature from static tissue within the angiogram.These 2D en face angiograms can be interpreted in a manner similar totraditional angiography techniques such as fluorescein angiography (FA)or indocyanine green (ICG) angiography, and are thus well-suited forclinical use.

Optical coherence tomography angiography has recently been used tovisualize choroidal neovascularization (CNV) in patients withage-related macular degeneration. Identification and quantification ofCNV area is important clinically for disease assessment. An automaticalgorithm for CNV area detection is presented herein. It relies ondenoising and a saliency detection model to overcome issues such asprojection artifacts and the heterogeneity of CNV. Qualitative andquantitative evaluation was performed on scans of 5 patients. Theautomated algorithm agrees well with manual delineation of CNV area.

Identification and quantification of CNV from OCT angiography datasetsposes a number technical challenges. First, OCT angiography issusceptible to shadowgraphic flow projection artifacts. Due to stronglight absorption of blood cells, blood vessels cast shadows in depth onstructural OCT. Reflectance amplitude decorrelation in the blood vesseldue to flow is carried in the shadow. As a result the vascular patternfrom the superficial inner retina is replicated on the deeper outerretina, compromising the clarity of the more deeply imaged structures.Second, while the effect of eye motion during the scan can be minimizedby subtracting bulk motion noise (Jia Y et al, 2012 supra; Jia Y et al,Ophthalmol 121, 1322-1332 (2014); incorporated herein by reference) andusing orthogonal registration (Kraus M et al, Biomed Opt Express 5,2591-2613 (2014); incorporated by reference herein), motion artifacts inthe form of horizontal or vertical lines may remain. Finally, theintrinsic complexity of CNV also makes automated detection difficult.The shape, size, location, and velocity of flow of the CNV can varybetween patients, and the boundary between CNV and what is not CNV, thebackground, can be hard to distinguish with conventional automateddetection techniques. Thus, accounting for artifacts and for thecomplexity of the CNV lesion are key problems which need to be solved.

Methods to segment and analyze vascular structures from fundus or FAimages have been based on structure enhancement filters (Frangi A et al,MICCAI '98, 130-137 (1998); Law M et al, ECCV, 368-382 (2008);incorporated by reference herein) and/or geodesic methods Chen D et al,in Scale Space and Variational Methods in Computer Vision, 270-281(2015); incorporated by reference herein). However, few works have beenpublished about segmentation of CNV from OCT angiography images (Jia Yet al, 2014 supra). Because the CNV lesion is dissimilar from projectionand motion artifacts, saliency based detection methods were employedherein (Borji A et al, IEEE Trans Pattern Anal Mach Intell 35, 185-207(2013); incorporated by reference herein). Briefly, saliency describesan abstraction of how the human visual system characterizes regions orobjects which stand out from their surrounding parts. Saliency basedmethods attempt to replicate this process for the detection of dominantobjects in a scene based on various image features. In this disclosure,an automatic segmentation algorithm, termed “saliency algorithm”, thatis dedicated to CNV recognition in outer retina en face angiograms fromOCT angiography is described.

Algorithm Overview

An overview of the disclosed algorithm is shown in FIG. 1. Apre-processing step was first performed to reduce projection artifactsfrom the outer retina. After denoising, the CNV region was moredistinctive. Vascular pattern recognition through a saliency modelfollowed. Finally, post-processing steps based on nonlinear filtering,thresholding, and morphological operations were applied to generate aCNV membrane mask. The following three sections will describe theprocess in detail. The algorithm was implemented with custom softwarewritten in Matlab 2011a (Mathworks, Natick, Mass.).

Pre-Processing

Retinal circulations are primarily transverse to the OCT light beam andare best visualized by projecting the volumetric data set as 2D en faceimages. Anatomical landmarks from structural OCT reflectance images wereused to guide semi-automated segmentation to separate circulations basedon depth (Tan O et al, Ophthalmol 115, 949-956 (2008); incorporatedherein by reference). Maximum flow projection between the internallimiting membrane (ILM) and outer plexiform layer (OPL) generated the enface inner retina angiogram, representing retinal circulation. Maximumflow projection between the outer boundary of OPL to BM generated the enface outer retina angiogram, normally an avascular region. CNV growsfrom the choroid through BM and often is directly adjacent to theretinal pigment epithelium (RPE). Inner retinal vessels project artifactonto the RPE, due to its high reflectance on structural OCT. Thisprojection artifact in the outer retina angiogram interferes with CNVdetection.

Angiographic projection and motion artifact ideally need to be minimizedprior to application of the saliency method. In previous work (Jia Y etal, 2014 supra), a binary large inner retinal vessel map was used tomask vessel projections on the outer retinal angiogram. However, it canbe difficult to determine the appropriate threshold for obtaining such avessel mask. If the threshold value is too low, the mask can containsmaller inner retinal vessels which can remove some useful informationin the CNV region. Alternatively, masking only large vessels may leavesmall inner retina vessel projections that are difficult todifferentiate as projection artifact versus CNV. In the algorithmdisclosed herein the inner retina angiogram is filtered by a 20×20 pixelGaussian filter with a sigma value of 0.1. The filtered innerretinaangiogram is then subtracted from the outer retina angiogram. Theresulting image better highlighted the CNV region, but still containedsmall, bright discrete noise areas.

Vascular Pattern Recognition

Because the CNV becomes more distinct in the outer retina angiogramafter artifact removal, saliency based detection is an effective androbust method to delineate its structure.

Detection accuracy depends on both the distinctiveness of the targetobject and the homogeneity and/or blurred degree of the background. As aresult of projection removal, some parts of the CNV region becamediscontinuous and fuzzy. Therefore, the saliency model should detect notonly the salient region and but also neighboring regions. Acontext-aware saliency detection method was employed to account for theabove issues. This method combines context-awareness and saliencydetection with the aim of detecting the prominent objects and the partsof the background that contain similar contextual information (GofermanS et al, IEEE Trans Pattern Anal Mach Intell 34, 1915-1926 (2012);incorporated herein by reference).

Context-aware saliency detection borrows from some basic principlesassociated with human visual attention: local low-level considerationssuch as brightness and contrast, global considerations to ignorereoccurring features, and visual organization rules regarding objectcenter(s) of gravity. For many image processing applications, local andglobal considerations incorporate color information, but OCT angiographyis simplified in the sense that it produces what can be consideredgrayscale en face images. In the present disclosure, brightness,orientation contrast, and positional distance were used together todefine a measure of distinctiveness. In the en face outer retinalangiogram, each pixel i was assessed. The local context of pixel i wasgiven by considering its surrounding pixels in a 7×7 pixel patchcentered on the pixel. The saliency of each pixel i depends on thedistinctiveness of its patch. The quantity d_(bright)(p_(i), p_(j)) wasdefined as the Euclidean distance between the summed intensities ofpatches p_(i) and p_(j) in the grayscale image, normalized to the range[0,1].

However, because there were still some small, bright non-CNV areas inthe projection-removed angiogram, brightness information alone was notsufficient to delineate CNV. Therefore, the local orientationinformation (Itti L et al, IEEE Trans Patt Anal Mach Intell 20,1254-1259 (1998); incorporated herein by reference) was incorporated toaid in the determination of what is of interest. The local orientationinformation was obtained utilizing Gabor filters, which are a product ofa cosine grating and 2D Gaussian envelope, at four preferredorientations θε{0°, 45°, 90°, 135°}. The size of the Gabor kernel was31×31 pixels. The Euclidean distance d_(orientation)(p_(i), p_(j))|_(θ)between patches p_(i) and p_(j) was calculated as the orientationcontrast at the corresponding orientation θ:

$\begin{matrix}{{{d_{orientation}\left( {p_{i},p_{j}} \right)} = {\frac{1}{N}{\sum{d_{orientation}\left( {p_{i},p_{j}} \right)}}}}}_{\theta} & (1)\end{matrix}$

where θ=0°, 45°, 90°, 135° and N=4. This was also normalized to therange [0,1]. In the projection-removed outer retina angiogram, CNVregions were grouped together. Thus, a metric d_(position)(p_(i), p_(j))was defined as the positional distance between patches p_(i) and p_(j).The distinctiveness between two patches was then defined as

$\begin{matrix}{{d\left( {p_{i},p_{j}} \right)} = \frac{{d_{bright}\left( {p_{i},p_{j}} \right)} + {d_{orientation}\left( {p_{i},p_{j}} \right)}}{2\left( {1 + {c \cdot {d_{position}\left( {p_{i},p_{j}} \right)}}} \right)}} & (2)\end{matrix}$

where c=3. The distinctiveness measure considers the local and globalinformation simultaneously. It is proportional to the difference inappearance represented by brightness and orientation contrast andinversely proportional to the positional distance. Pixel i is consideredsalient when d(p_(i), p_(j)) is high for all j.

Multi-scale saliency detection was further incorporated to decrease thesaliency of background and enhance the contrast between salient andnon-salient areas. Typically background patches are more likely to besimilar at multiple scales, while the dominant object is salient andcould have similar patches at a few scales but not at all of them.Because using multiple scales increases computation time, a simplifiedapproach using only comparisons to only the K most similar patches wasadopted. The saliency value of pixel i at a single-scale r was thendefined as

$\begin{matrix}{S_{i}^{r} = {1 - {\exp \left\{ {{- \frac{1}{K}}{\sum\limits_{k = 1}^{K}{d\left( {p_{i}^{r},q_{k}^{r}} \right)}}} \right\}}}} & (3)\end{matrix}$

where q_(K) belongs to the identified K most similar patches and K=65.When searching for the K most similar patches, patches of 7×7 with 50percent overlap were considered.

The saliency of pixel i at scale r was determined from the K mostsimilar patches at multiple scales R_(q)={r, (½)r, (¼)r}. At each scaler, the saliency map was normalized to the range [0,1] and interpolatedback to original image size of 304×304 pixels. Equation 3 was refined as

$\begin{matrix}{S_{i}^{r} = {1 - {\exp \left\{ {{- \frac{1}{K}}{\sum\limits_{k = 1}^{K}{d\left( {p_{i}^{r},q_{k}^{r_{k}}} \right)}}} \right\}}}} & (4)\end{matrix}$

where r_(k)εR_(q). The final saliency value for pixel i was the mean ofall patches p_(i) at different scale r.

$\begin{matrix}{{\overset{\_}{S}}_{i} = {\frac{1}{M}{\sum\limits_{r \in R}S_{i}^{r}}}} & (5)\end{matrix}$

Four scales were used, R={100%, 80%, 50%, 30%}.

A final consideration was that areas close to the attention foci aresupposed to be more distinctive than those regions far away. The visualcontextual effect was simulated. A threshold operation was applied toextract the most attended localized areas from the saliency map, whichcontained all the pixels with their saliency value greater than athreshold of 0.8. The saliency value of pixels outside the most attendedlocalized areas was redefined according to its Euclidean distanced_(foci) ^(r)(i) of position to the closest attended pixel at scale r,normalized to range [0,1]. The saliency of each pixel was modified as

$\begin{matrix}{{\hat{S}}_{i} = {\frac{1}{M}{\sum\limits_{r \in R}{S_{i}^{r}\left( {1 - {d_{foci}^{r}(i)}} \right)}}}} & (6)\end{matrix}$

After this step, the saliency value of the interesting background in theneighborhood of the salient objects will be increased. This allowed forthe inclusion of the neighboring regions to ensure all the CNV wasdetected.

Generation of the Vascular Mask

The integration of multi-scale enhancement and context-awareness led toa saliency map that approximated the CNV region. However, it wasdifficult to determine the threshold to extract the CNV region from thesaliency map as the map was usually blurred at boundaries. To addressthe issue of blurred boundaries, a Laplacian edge detection filter wasfirst used on the saliency map. In parallel, the bilateral filterproposed by (Tomasi C et al, IEEE Sixth Int Conf Comp Vis Bombay (1998),839-846; incorporated herein by reference) was used to smooth within thetarget region and preserve the boundary. This bilateral filter was aweighted average operation process. Unlike the traditional Gaussianfilter which utilizes only position information as the weight, theweight of the shift-invariant Gaussian filter belonging to the bilateralfilter contains both position distance and intensity information. Afterbilateral filtering, the boundary information detected by the Laplacianedge detection operator was used to enhance the boundaries. Otsu'sthreshold method was used to extract the rough CNV region, after whichsome small, discrete regions still remained. Morphological operationswere then used to remove small areas (<80 pixels) and fill holes.Finally, the CNV membrane mask was obtained. It was a binary image bywhich the original en face outer retina angiogram was multiplied toextract the CNV. A threshold operator was used to calculate the CNVarea.

EXAMPLES Example 1—Methods Data Collection

Patients were selected from those diagnosed with neovascular AMD at theCasey Eye Institute Retina Service based on clinical presentation,examination, and fluorescein angiography. Patients were enrolled afterinformed consent in accordance with an Institutional Review Board/EthicsCommittee-approved protocol at Oregon Health & Science University and incompliance with the Declaration of Helsinki.

Two volumetric datasets were collected from single eyes of 5 patientswith neovascular AMD. All of the data was collected using a commercial70 kHz spectral domain OCT system with a center wavelength of 840 nm(RTVue-XR, Optovue, Calif.). The macular angiography scan protocol for asingle volumetric dataset contained 2 scans covering a 3×3 mm area. Eachscan comprised of 304×304×2 A-scans acquired in less than 3 seconds. Thefast scanning direction was in the horizontal direction for the firstscan and in the vertical direction for the second. The SSADA algorithmwas applied to detect flow between the 2 consecutive B-scans at the samelocation (Jia Y et al, Opt Express 20, 4710-4725 (2012); Gao S et al,Opt Letters 40, 2305-2308 (2015); incorporated herein by reference). Thetwo scans were then registered and merged through an orthogonalregistration algorithm (Kraus M et al, 2014, supra; incorporated byreference herein).

Sixteen participants were recruited. Data from 6 participants wereexcluded due to low image quality (structural OCT signal strength index<50), severe motion artifacts, and/or shadowing due to pigmentepithelial detachment. Data from 3 other participants were excludedbecause an experienced grader could not identify the presence of CNV onOCT angiography. Data from the remaining 7 participants were used inthis study.

Verification of Results

The algorithm results were compared to output from the algorithm used ina previous study, termed the “previous algorithm” (Jia Y et al, 2014supra), and results from manual delineation of the CNV. The within-visitrepeatability was assessed for the automated previous algorithmdescribed, the disclosed automated saliency algorithm, and manualdelineation using coefficient of variation (CV) and intraclasscorrelation (ICC). For manual delineation, the CNV was contoured by anexperienced grader, and a threshold operator was used to calculate theCNV area.

To compare the results from the two automated algorithms to that frommanual delineation, the Jaccard similarity metric was used, which isdefined as

J(I _(s) ,I _(m))=|I _(s) ∩I _(m) |/|I _(s) ∪I _(m)|  (7)

where I_(s) is the segmentation result from one of the automatedpipelines and I_(m) is the result from manual delineation. The Jaccardcoefficient ranges from 0 to 1, where 1 denotes that the two wereidentical and 0 if they were completely dissimilar. Using the manualdelineation results as the standard, errors rates were also computed.False positive error was the ratio of the total number of automaticallysegmented pixels that were not included in the manual segmentationresult to the total number of manually segmented pixels. False negativeerror was the ratio of the total number of manually segmented pixelsthat were not included in the automatic segmentation result to the totalnumber of manually segmented pixels (Lee J et al, Comp Methods ProgramsBiomed 88, 26-38 (2007); incorporated herein by reference).

Results for a Single Patient

The en face outer retinal angiogram from a participant with neovascularAMD was used to show the workflow of the previous algorithm and thesaliency algorithm. FIG. 2A shows the original outer retina angiogramwith CNV and artifacts. The left column illustrates the process usingthe previous algorithm. The mask of inner retina vessels shown in FIG.2B1 was used to remove the large vessels projections from the outerretina. The result shown in FIG. 2C1 still has some small vesselprojections and motion artifacts. The previous algorithm then applies aGaussian filter to reduce the remaining artifacts (FIG. 2D1). For thefinal step, a threshold operator was used to extract the CNV area (FIG.2E1). However, artifacts still remained after the mask subtraction andGaussian filtering, leading some artifacts to be misclassified as CNV.

In the disclosed saliency algorithm, the inner retina angiogram wasfirst smoothed by a 20×20 pixel Gaussian filter to produce the filteredinner retina shown in FIG. 2B2. By subtracting the filtered inner retinafrom the outer retina most of the projection artifacts were removed(FIG. 2C2) and some CNV signal was also reduced. However, the CNV areabecame more distinctive in the outer retina. Then, a context-awaresaliency model based on brightness, orientation, and positioninformation was used to detect the CNV region (FIG. 2C2). The saliencycalculation was done at multiple scales (FIG. 2F1) and combined into asingle saliency map (FIG. 2D2). To aid in the segmentation of the CNV,an edge-enhanced nonlinear filter was used to smooth the CNV region andenhance the boundary. Next, Otsu's method was used to determine thethreshold. Finally, morphological operations were used to remove smallisolated regions and fill holes to obtain the CNV membrane mask. Thesepost-processing steps are shown in FIG. 2F2. The en face outer retinalangiogram was multiplied by the CNV membrane mask, and a thresholdoperator was used to determine the CNV area (FIG. 2E2).

Results from Seven Patients

The results from the disclosed saliency algorithm were compared withresults from the previous automated algorithm and with results obtainedby manual delineation of the CNV. Scans of a single eye of seven (7)patients with AMD were analyzed. Two volumetric datasets from each eyewere evaluated to assess within-visit repeatability. The results fromone dataset of each subject are shown in FIG. 3 and FIG. 4. The casesincluded both type I and type II (participants #1, 5) CNV membranes witha wide range of sizes. An expert human grader delineated the boundary ofthe CNV membrane on the en face maximum flow projection angiogram of theouter retinal slab, while also viewing the inner retinal angiogram. Thesaliency algorithm automatically outlined the CNV boundary andcalculated CNV area. The algorithm required 17.5 seconds to execute onan Intel Xeon CPU (E3-1226, 3.3 Ghz), of which 94% of the time was spenton generating the saliency map. FIGS. 3B1 to 3B6 show the manualdelineation results whereby a grader contoured the CNV. Because novessel projection removal was utilized in this method, the manualdelineation results are potentially overestimates. FIGS. 3C1 to 3C6 showthe results of the disclosed saliency algorithm. FIGS. 3D1 to 3D6 arethe segmented results produced by the previous algorithm.

Qualitatively, the results from the disclosed saliency algorithm wereclosely matched to those from manual delineation. However, the saliencyalgorithm tended to include less area from the CNV due to the projectionartifact removal step. This is highlighted in the results fromparticipant #5 in FIG. 4 (compare the respective regions as indicated byyellow arrows in FIGS. 4(C) and 4(D)). The previous algorithm alsoidentified similar shapes for the CNV membranes, but did not cleanlyremove scattered background noise due to projection and motionartifacts. The continuity of the CNV network was also often broken up bythe higher decorrelation threshold used. It was clear that the previousalgorithm differed more from manual CNV grading, and this poor agreementwas reflected in the Jaccard similarity metric, false negative, andfalse positive CNV pixel identification rates (Table 1). In contrast,the saliency algorithm agreed well with manual grading. The saliencyalgorithm was significantly better than the previous algorithm in all

TABLE 1 Agreement Between Automated Algorithms and Manual Grading ofChoroidal Neovascularization Previous Disclosed Saliency AlgorithmAlgorithm P-value Jaccard similarity metric 0.157 ± 0.059 0.834 ± 0.125<0.001 False positive error 0.120 ± 0.066 0.043 ± 0.046 0.001 Falsenegative error 0.826 ± 0.059 0.134 ± 0.109 <0.001 Measures of agreementwere computed on a pixel-by-pixel basis from graded en face angiogramsof choroidal neovascularization. Mean ± standard deviation of theJaccard similarity metric and error rates were computed from 7participants. P-values were based on the paired Wilcoxon rank-sum test.

Repeatability of CNV area measurement was calculated from the 2 sets ofOCT angiography scans obtained from each participant. All methods hadrelatively good repeatability as measured by CV and ICC (Table 2).

TABLE 2 Repeatability of Choroidal Neovascularization QuantificationManual Proposed Saliency Algorithm Previous Algorithm CV 3.90% 6.70%7.15% ICC 0.998 0.992 0.993 Abbreviations: coefficient of variation(CV); intraclass correlation (ICC), Data was from 7 participants.

Superior Performance of Saliency-Based Model in 840 nm OCT System

The previous algorithm had worked well using OCT angiography obtainedusing a 1050 nm swept-source OCT system. The projection artifact fromthe inner retinal vessel onto the outer retina was much sparser at thatlonger wavelength and consisted of mainly a large vessel pattern;therefore the CNV pattern was not as severely disrupted by theprojection artifact. However, in the 840 nm spectral domain OCT systememployed in the examples described herein, the shadow cast by the innerretinal vessels was much stronger due to the shorter wavelength used bythe device. Therefore the projection artifact included not only largerretinal vessels but also fine capillary patterns. The removal of thismuch denser projection artifact disrupted the CNV pattern much moreseverely. Therefore the saliency map was needed to restore the CNVoutline. The Gaussian filter and thresholding employed by the previousalgorithm were not adequate to the task. FIG. 5 illustrates the problemwith dense inner retinal projection. Both the subtraction of allprojection artifacts and the saliency map steps were necessary to obtaina clean outline of the CNV.

Summary of Example 1

OCT angiography is a new imaging approach to visualizing CNV. Thedisclosed algorithm for CNV area quantification involves 4 main steps:(1) minimize projection artifacts in the outer retina angiogram bysubtracting the inner retinal vessel pattern; (2) identify the CNV areaby a context-aware saliency model based on brightness, orientation, andposition information to identify CNV; (3) enhance the saliency map bynonlinear filtering, and (4) calculate CNV area from flow pixels withinthe CNV boundary. It was shown that the disclosed algorithm could detectand quantify the CNV in neovascular AMD cases with a variety of CNVpatterns. The saliency algorithm agreed with expert human grading muchbetter than the previous algorithm. The previous algorithm had a notablyhigh false negative rate of 0.826 (Table 1) because the steps ofsubtracting large inner retinal vessel projection and thresholding alsoremoved flow signal in the CNV. Identification of the CNV through thesaliency map helped to restore the outline of the CNV after removal ofprojection artifact.

Example 2—Optical Coherence Tomography Angiography Image ProcessingSystem

FIG. 6 schematically shows an example system 600 for OCT imageprocessing in accordance with various embodiments. System 600 comprisesan OCT system 602 configured to acquire an OCT image comprising OCTinterferograms and one or more processors or computing systems 604 thatare configured to implement the various processing routines describedherein. OCT system 600 can comprise an OCT system suitable for OCTangiography applications, e.g., a swept source OCT system.

In various embodiments, an OCT system can be adapted to allow anoperator to perform various tasks. For example, an OCT system can beadapted to allow an operator to configure and/or launch various ones ofthe herein described methods. In some embodiments, an OCT system can beadapted to generate, or cause to be generated, reports of variousinformation including, for example, reports of the results of scans runon a sample.

In embodiments of OCT systems comprising a display device, data and/orother information can be displayed for an operator. In embodiments, adisplay device can be adapted to receive an input (e.g., by a touchscreen, actuation of an icon, manipulation of an input device such as ajoystick or knob, etc.) and the input can, in some cases, becommunicated (actively and/or passively) to one or more processors. Invarious embodiments, data and/or information can be displayed, and anoperator can input information in response thereto.

In some embodiments, the above described methods and processes can betied to a computing system, including one or more computers. Inparticular, the methods and processes described herein, e.g., the methoddepicted in FIGS. 1-6 described above, can be implemented as a computerapplication, computer service, computer API, computer library, and/orother computer program product.

FIG. 7 schematically shows a non-limiting computing device 700 that canperform one or more of the above described methods and processes. Forexample, computing device 700 can represent a processor included insystem 600 described above, and can be operatively coupled to, incommunication with, or included in an OCT system or OCT imageacquisition apparatus. Computing device 700 is shown in simplified form.It is to be understood that virtually any computer architecture can beused without departing from the scope of this disclosure. In differentembodiments, computing device 700 can take the form of a microcomputer,an integrated computer circuit, printed circuit board (PCB), microchip,a mainframe computer, server computer, desktop computer, laptopcomputer, tablet computer, home entertainment computer, networkcomputing device, mobile computing device, mobile communication device,gaming device, etc.

Computing device 700 includes a logic subsystem 702 and a data-holdingsubsystem 704. Computing device 700 can optionally include a displaysubsystem 706, a communication subsystem 708, an imaging subsystem 710,and/or other components not shown in FIG. 7. Computing device 700 canalso optionally include user input devices such as manually actuatedbuttons, switches, keyboards, mice, game controllers, cameras,microphones, and/or touch screens, for example.

Logic subsystem 702 can include one or more physical devices configuredto execute one or more machine-readable instructions. For example, thelogic subsystem can be configured to execute one or more instructionsthat are part of one or more applications, services, programs, routines,libraries, objects, components, data structures, or other logicalconstructs. Such instructions can be implemented to perform a task,implement a data type, transform the state of one or more devices, orotherwise arrive at a desired result.

The logic subsystem can include one or more processors that areconfigured to execute software instructions. For example, the one ormore processors can comprise physical circuitry programmed to performvarious acts described herein. Additionally or alternatively, the logicsubsystem can include one or more hardware or firmware logic machinesconfigured to execute hardware or firmware instructions. Processors ofthe logic subsystem can be single core or multicore, and the programsexecuted thereon can be configured for parallel or distributedprocessing. The logic subsystem can optionally include individualcomponents that are distributed throughout two or more devices, whichcan be remotely located and/or configured for coordinated processing.One or more aspects of the logic subsystem can be virtualized andexecuted by remotely accessible networked computing devices configuredin a cloud computing configuration.

Data-holding subsystem 704 can include one or more physical,non-transitory, devices configured to hold data and/or instructionsexecutable by the logic subsystem to implement the herein describedmethods and processes. When such methods and processes are implemented,the state of data-holding subsystem 704 can be transformed (e.g., tohold different data).

Data-holding subsystem 704 can include removable media and/or built-indevices. Data-holding subsystem 704 can include optical memory devices(e.g., CD, DVD, HD-DVD, Blu-Ray Disc, etc.), semiconductor memorydevices (e.g., RAM, EPROM, EEPROM, etc.) and/or magnetic memory devices(e.g., hard disk drive, floppy disk drive, tape drive, MRAM, etc.),among others. Data-holding subsystem 704 can include devices with one ormore of the following characteristics: volatile, nonvolatile, dynamic,static, read/write, read-only, random access, sequential access,location addressable, file addressable, and content addressable. In someembodiments, logic subsystem 702 and data-holding subsystem 704 can beintegrated into one or more common devices, such as an applicationspecific integrated circuit or a system on a chip.

FIG. 7 also shows an aspect of the data-holding subsystem in the form ofremovable computer-readable storage media 712, which can be used tostore and/or transfer data and/or instructions executable to implementthe herein described methods and processes. Removable computer-readablestorage media 712 can take the form of CDs, DVDs, HD-DVDs, Blu-RayDiscs, EEPROMs, flash memory cards, USB storage devices, and/or floppydisks, among others.

When included, display subsystem 706 can be used to present a visualrepresentation of data held by data-holding subsystem 704. As the hereindescribed methods and processes change the data held by the data-holdingsubsystem, and thus transform the state of the data-holding subsystem,the state of display subsystem 706 can likewise be transformed tovisually represent changes in the underlying data. Display subsystem 706can include one or more display devices utilizing virtually any type oftechnology. Such display devices can be combined with logic subsystem702 and/or data-holding subsystem 704 in a shared enclosure, or suchdisplay devices can be peripheral display devices.

When included, communication subsystem 708 can be configured tocommunicatively couple computing device 700 with one or more othercomputing devices. Communication subsystem 708 can include wired and/orwireless communication devices compatible with one or more differentcommunication protocols. As non-limiting examples, the communicationsubsystem can be configured for communication via a wireless telephonenetwork, a wireless local area network, a wired local area network, awireless wide area network, a wired wide area network, etc. In someembodiments, the communication subsystem can allow computing device 700to send and/or receive messages to and/or from other devices via anetwork such as the Internet.

When included, imaging subsystem 710 can be used acquire and/or processany suitable image data from various sensors or imaging devices incommunication with computing device 700. For example, imaging subsystem710 can be configured to acquire OCT image data, e.g., interferograms,as part of an OCT system, e.g., OCT system 602 described above. Imagingsubsystem 710 can be combined with logic subsystem 702 and/ordata-holding subsystem 704 in a shared enclosure, or such imagingsubsystems can comprise periphery imaging devices. Data received fromthe imaging subsystem can be held by data-holding subsystem 704 and/orremovable computer-readable storage media 712, for example.

It is to be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein can represent one or more of any number ofprocessing strategies. As such, various acts illustrated can beperformed in the sequence illustrated, in other sequences, in parallel,or in some cases omitted. Likewise, the order of the above-describedprocesses can be changed.

The subject matter of the present disclosure includes all novel andnonobvious combinations and subcombinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof.

1-10. (canceled)
 11. A method of identifying a choroidalneovascularization (CNV) region in an optical coherence tomography (OCT)angiogram comprising: obtaining an inner retina angiogram and an outerretina angiogram that correspond to different regions of a retina;subtracting the inner retina angiogram from the outer retina angiogram,thereby generating a subtracted outer retina angiogram; generating abinary vascular mask from the subtracted outer retina angiogram; andmultiplying the binary vascular mask by the outer retina angiogram,thereby identifying the CNV region in the outer retina angiogram. 12.The method of claim 11, further comprising calculating a CNV area of theCNV region.
 13. The method of claim 11, wherein generating the binaryvascular mask from the subtracted outer retina angiogram comprises:generating a saliency map of CNV in the subtracted outer retinaangiogram; and generating the binary vascular mask from the saliencymap.
 14. The method of claim 13, wherein the saliency map of CNV isgenerated using pattern recognition.
 15. The method of claim 14, whereinthe generation of a saliency map by pattern recognition comprisescalculating a saliency model that incorporates brightness, orientationcontrast, and positional distance as calculated by:${{{d_{orientation}\left( {p_{i},p_{j}} \right)} = {\frac{1}{N}{\sum{d_{orientation}\left( {p_{i},p_{j}} \right)}}}}}_{\theta}$where d_(orientation)(p_(i), p_(j)) is the orientation contrast betweenpatches p_(i) and p_(j) of the subtracted outer retinal angiogram,d_(orientation)(p_(i), p_(j))|_(θ) is a Euclidean distance between thepatches p_(i) and p_(j) at a corresponding orientation θ, θ=0°, 45°,90°, 135° and N=4, wherein d_(orientation)(p_(i), p_(j)) is normalizedto the range [0,1]; and${d\left( {p_{i},p_{j}} \right)} = \frac{{d_{bright}\left( {p_{i},p_{j}} \right)} + {d_{orientation}\left( {p_{i},p_{j}} \right)}}{2\left( {1 + {c \cdot {d_{position}\left( {p_{i},p_{j}} \right)}}} \right)}$wherein d(p_(i), p_(j)) is a distinctiveness between patches p_(i) andp_(j), d_(position)(p_(i), p_(j)) is a positional distance betweenpatches p_(i) and p_(j), d_(bright)(p_(i), p_(j)) is Euclidean distancebetween summed intensities of the patches p_(i) and p_(j), normalized tothe range [0,1], and c equals three.
 16. The method of claim 15, furthercomprising performing multiscale enhancement of the saliency model bycalculating saliency maps at multiple scales and combining the saliencymaps at multiple scales.
 17. The method of claim 15, further comprisingmodifying the saliency map to simulate context-awareness by: identifyinglocalized areas from the saliency map for which all pixels have arespective saliency value greater than a threshold; and redefiningsaliency values of pixels outside the localized areas according to aEuclidean distance of position to a closest pixel included in one of thelocalized areas.
 18. The method of claim 13, wherein generating thevascular mask from the saliency map comprises applying a Laplacian edgedetection filter to the saliency map, thereby generating anedge-enhanced saliency map.
 19. The method of claim 18, whereingenerating the edge-enhanced saliency map further comprises using anonlinear filter to smooth the saliency map within a target region andpreserve a boundary of the saliency map, thereby producing anedge-enhanced saliency map.
 20. The method of claim 18, furthercomprising thresholding the edge-enhanced saliency map to extract arough CNV region; and applying morphological operators to the rough CNVregion to remove small areas and fill holes, thereby generating thebinary vascular mask.
 21. The method of claim 20, wherein thethresholding includes using Otsu's method.
 22. The method of claim 11,wherein the inner retinal angiogram is a two-dimensional (2D) en faceinner retinal angiogram and the outer retinal angiogram is a 2D en faceouter retinal angiogram.
 23. The method of claim 22, further comprisingobtaining a set of cross-sectional OCT angiograms of the retina;separating the set of cross sectional OCT angiograms into an inner setof cross-sectional angiograms and an outer set of cross-sectionalangiograms based on one or more geographical markers; projecting maximumflow values of the inner set of cross-sectional angiograms along anaxial (Z) direction onto an X-Y plane, thereby generating the 2D en faceinner retina angiogram; and projecting maximum flow values along theaxial (Z) direction of the outer set of cross-sectional angiograms ontothe X-Y plane, thereby generating the 2D en face outer retina angiogram.24. The method of claim 23, wherein the inner set of cross-sectionalangiograms comprises flow values of the cross-sectional angiogramslocated between an internal limiting membrane and an outer plexiformlayer, and wherein the outer set of cross-sectional angiograms comprisesflow values of the cross-sectional angiograms located between the outerplexiform layer and Bruch's membrane.
 25. The method of claim 11,further comprising applying a denoising filter to the inner retinaangiogram prior to subtracting the inner retinal angiogram from theouter retinal angiogram.
 26. The method of claim 11, wherein thedenoising filter comprises a 20×20 pixel Gaussian filter or low passfilter.
 27. A method of determining a choroidal neovascularization (CNV)area in an optical coherence tomography (OCT) angiogram comprising:generating a saliency map of CNV from a two-dimensional (2D) en faceouter retina angiogram using pattern recognition; generating a binaryvascular mask from the saliency map; multiplying the binary vascularmask by the 2D en face outer retina angiogram, thereby identifying a CNVregion in the outer retina angiogram; and calculating the CNV area ofthe CNV region.
 28. The method of claim 27, wherein the generation of asaliency map by pattern recognition comprises calculating a saliencymodel that incorporates brightness, orientation contrast, and positionaldistance as calculated by:${{{d_{orientation}\left( {p_{i},p_{j}} \right)} = {\frac{1}{N}{\sum{d_{orientation}\left( {p_{i},p_{j}} \right)}}}}}_{\theta}$where d_(orientation)(p_(i), p_(j))|_(θ) is a Euclidean distance betweenpatches p_(i) and p_(j) of the subtracted outer retinal angiogram at acorresponding orientation θ, θ=0°, 45°, 90°, 135° and N=4, whereind_(orientation)(p_(i), p_(j)) is normalized to the range [0,1]; and${d\left( {p_{i},p_{j}} \right)} = \frac{{d_{bright}\left( {p_{i},p_{j}} \right)} + {d_{orientation}\left( {p_{i},p_{j}} \right)}}{2\left( {1 + {c \cdot {d_{position}\left( {p_{i},p_{j}} \right)}}} \right)}$wherein d(p_(i), p_(j)) is a distinctiveness between patches p_(i) andp_(j), d_(position)(p_(i), p_(j)) is a positional distance betweenpatches p_(i) and p_(j), d_(bright)(p_(i), p_(j)) is Euclidean distancebetween summed intensities of the patches p_(i) and p_(j), normalized tothe range [0,1], and c equals three.
 29. The method of claim 28, furthercomprising performing multiscale enhancement of the saliency model bycalculating saliency maps at multiple scales and combining the saliencymaps at multiple scales.
 30. The method of claim 28, further comprisingmodifying the saliency map to simulate context-awareness by: identifyinglocalized areas from the saliency map for which all pixels have arespective saliency value greater than a threshold; and redefiningsaliency values of pixels outside the localized areas according to aEuclidean distance of position to a closest pixel included in one of thelocalized areas.
 31. The method of claim 27, further comprisingobtaining a 2D en face inner retina angiogram and the 2D en face outerretina angiogram that correspond to different regions of a retina; andsubtracting the 2D en face inner retina angiogram from the 2D en faceouter retina angiogram prior to generating the saliency map from theouter retina angiogram.
 32. The method of claim 31, further comprisingobtaining a set of cross-sectional OCT angiograms of the retina;separating the set of cross sectional OCT angiograms into an inner setof cross-sectional angiograms and an outer set of cross-sectionalangiograms based on one or more geographical markers; projecting maximumflow values of the inner set of cross-sectional angiograms along anaxial (Z) direction onto an X-Y plane, thereby generating the 2D en faceinner retina angiogram; and projecting maximum flow values along theaxial (Z) direction of the outer set of cross-sectional angiograms ontothe X-Y plane, thereby generating the 2D en face outer retina angiogram.33. A system for determining a choroidal neovascularization (CNV) areain an optical coherence tomography (OCT) angiogram, the systemcomprising: an OCT system configured to acquire an OCT dataset for aretina; a logic subsystem; and a data holding subsystem comprisingmachine-readable instructions stored thereon that are executable by thelogic subsystem to: obtain an inner retina angiogram and an outer retinaangiogram from the OCT dataset, wherein the inner retina angiogram andthe outer retina angiogram correspond to different regions of theretina; subtract the inner retina angiogram from the outer retinaangiogram to generate a subtracted outer retina angiogram; generate abinary vascular mask from the subtracted outer retina angiogram; andapply the binary vascular mask to the outer retina angiogram to identifya CNV region in the outer retina angiogram; and calculate a CNV area ofthe CNV region.
 34. The system of claim 33, wherein, to generate thebinary vascular mask from the subtracted outer retina angiogram, thelogic subsystem is to: generate a saliency map of CNV in the subtractedouter retina angiogram; and generate the binary vascular mask from thesaliency map.
 35. The system of claim 34, wherein the saliency map ofCNV is generated using pattern recognition.
 36. The system of claim 35,wherein, to generate the saliency map by pattern recognition, the logicsubsystem is to use a saliency model that incorporates brightness,orientation contrast, and positional distance as calculated by:${{{d_{orientation}\left( {p_{i},p_{j}} \right)} = {\frac{1}{N}{\sum{d_{orientation}\left( {p_{i},p_{j}} \right)}}}}}_{\theta}$where d_(orientation)(p_(i), p_(j)) is the orientation contrast betweenpatches p_(i) and p_(j) of the subtracted outer retinal angiogram,d_(orientation)(p_(i), p_(j))|_(θ) is a Euclidean distance between thepatches p_(i) and p_(j) at a corresponding orientation θ, θ=0°, 45°,90°, 135° and N=4, wherein d_(orientation)(p_(i), p_(j)) is normalizedto the range [0,1]; and${d\left( {p_{i},p_{j}} \right)} = \frac{{d_{bright}\left( {p_{i},p_{j}} \right)} + {d_{orientation}\left( {p_{i},p_{j}} \right)}}{2\left( {1 + {c \cdot {d_{position}\left( {p_{i},p_{j}} \right)}}} \right)}$wherein d(p_(i), p_(j)) is a distinctiveness between patches p_(i) andp_(j), d_(position)(p_(i), p_(j)) is a positional distance betweenpatches p_(i) and p_(j), and d_(bright)(p_(i), p_(j)) is Euclideandistance between summed intensities of the patches p_(i) and p_(j),normalized to the range [0,1].
 37. The system of claim 36, wherein theinstructions are further to cause the logic subsystem to performmultiscale enhancement of the saliency model by calculating saliencymaps at multiple scales and combining the saliency maps at multiplescales.
 38. The system of claim 36, wherein the instructions are furtherto cause the logic subsystem to: identify localized areas from thesaliency map for which all pixels have a respective saliency valuegreater than a threshold; and redefine saliency values of pixels outsidethe localized areas according to a Euclidean distance of position to aclosest pixel included in one of the localized areas.
 39. The system ofclaim 33, wherein the inner retinal angiogram is a two-dimensional (2D)en face inner retinal angiogram and the outer retinal angiogram is a 2Den face outer retinal angiogram.
 40. The system of claim 39, wherein, toobtain the 2D en face inner retinal angiogram and the 2D en face outerretinal angiogram, the logic subsystem is to: obtain a set ofcross-sectional OCT angiograms of the retina from the OCT dataset;separate the set of cross sectional OCT angiograms into an inner set ofcross-sectional angiograms and an outer set of cross-sectionalangiograms based on one or more geographical markers; project maximumflow values of the inner set of cross-sectional angiograms along anaxial (Z) direction onto an X-Y plane to obtain the 2D en face innerretina angiogram; and project maximum flow values along the axial (Z)direction of the outer set of cross-sectional angiograms onto the X-Yplane to obtain the 2D en face outer retina angiogram.
 41. The system ofclaim 40, wherein the inner set of cross-sectional angiograms comprisesflow values of the cross-sectional angiograms located between aninternal limiting membrane and an outer plexiform layer, and wherein theouter set of cross-sectional angiograms comprises flow values of thecross-sectional angiograms located between the outer plexiform layer andBruch's membrane.
 42. The system of claim 33, wherein the instructionsare further to cause the logic subsystem to apply a denoising filter tothe inner retina angiogram prior to subtraction of the inner retinalangiogram from the outer retinal angiogram.